Climate · Material · Biological Intelligence OS

One sample. Four spectroscopies. A quantum state, and a routing decision you can audit.

FTIR molecular fingerprints, NIR bulk composition, HPLC purity and Raman vibration streams are fused by cross-modal attention, encoded into a quantum feature map, and resolved into a process route by QUBO minimisation — across climate tech, clean tech, plastic and glass circularity, food waste, agri-to-energy and water recycling.

Honest framing: every stream on this page is a deterministic synthetic simulation and the quantum stage is a classical quantum-feature-map emulator. It demonstrates the pipeline — it is not a hardware quantum run and not a field-validated performance claim.

Architecture

ClimateFarm AI-Quantum Neural OS · seven layers

Physical / biological

assets under measurement

FarmCropSoilWaterBiomassPlasticGlass

Multi-modal sensing

instrument layer

IoT EC/VWCWeatherSatellite NDVI/SARFTIRNIRHPLCRamanVision

Signal intelligence

conditioning + features

Baseline correctionSavitzky-GolayFFTWaveletPeak detectionPCA/PLSEMG fittingAnomaly detection

AI representation

learned embeddings

1D-ResNetTransformerAutoencoderPLS-RGBMPINNVision modelMultimodal attentionUncertaintyTemporal twin

Quantum / quantum-ready

feature map + optimisation

Angle encodingFeature mapVQCQKAQuantum kernelQUBO

Neural decision OS

actions, not numbers

QualityPurityDiseaseMaterial IDContaminationCarbonCircularityIrrigationSortingWtE routingWater reuse

Delivery

who receives the decision

Farmer / FPO nudgeProcess controlMRV / ESG / traceability

Farmer / FPO · AI nudge

Recycler / processor · process control

Enterprise / regulator · MRV, ESG, traceability

Use case

Pick the circularity stream

Live simulator

♻️ Plastic Circularity · sample state

Sample inputs

Moisture / water fraction8%Contamination / foreign material5.0%Degradation / oxidation14%Target compound / polymer purity90%Microbial / pathogen proxy6%Batch mass (kg)1,040 kgDistance to recovery hub34 kmInstrument drift8%

FTIR · A(ν̃) = −log₁₀(I/I₀)

Amide I 1650 · Amide II 1550 · C=O 1730 · phosphate 1080

NIR · A = log₁₀(1/R)

O–H 1450/1940 · C–H 1720/2310 · cellulose 2100

HPLC · S(t) = Σ Pᵢ(t) + ε(t)

EMG(t; μ, σ, τ) deconvolution · target 4.7 min

Raman · Δν̃ = 1/λ₀ − 1/λₛ

785 nm · ring 1000 · CH₂ 1450 · biofilm 1610

Quality score

62.5

/ 100

HPLC purity

80.2%

target peak area

Contamination P

22.4%

Oxidation index

0.53

A₁₇₃₀ / A₁₀₈₀

Circularity grade

score 78/100

Pipeline latency

16 ms

simulated, not measured

AI representation

Cross-modal attention, not concatenation

Z = Attention(z_F, z_N, z_H, z_R, z_IoT, z_Sat)

FTIR · molecular fingerprint21.2% · z=[0.15, 0.42, 0.70]

NIR · bulk composition18.6% · z=[-0.29, -0.34, 0.65]

HPLC · purity & residues13.8% · z=[0.54, -0.01, -0.12]

Raman · vibration & micro-spatial9.3% · z=[0.20, -0.34, -0.09]

IoT · EC / VWC / weather17.1% · z=[-0.44, 0.68, 0.08]

Satellite · NDVI / SAR20.0% · z=[0.58, 0.57, 0.48]

Z = [0.113, 0.223, 0.354] · aggregate SNR 2.1

Quantum encoding · ⟨Z⟩ per qubit

Encoded feature	θ (rad)	⟨Z⟩
FTIR amide	0.42	+0.91
FTIR carbonyl OI	0.16	+0.75
NIR water	-1.99	-0.34
HPLC purity	1.61	-0.04
HPLC impurity	-0.56	+0.77
Raman 785 contam.	-1.24	+0.22
Microbial proxy	-2.35	-0.55
Degradation	-1.92	-0.34

Quantum-ready layer

State superposition and kernel alignment

|ψ⟩ measurement · Σ|amplitude|² = 1

α|virgin⟩0.592 → P = 35.0%

β|recyclable⟩0.620 → P = 38.4%

γ|contaminated⟩0.254 → P = 6.5%

δ|degraded⟩0.448 → P = 20.0%

The measurement collapse is the routing decision — the basis is the process catalogue for this stream.

K(xᵢ,xⱼ) = |⟨φ(xᵢ)|φ(xⱼ)⟩|² vs reference library

Healthy / food-grade reference0.652

Stress / mixed-stream reference0.037

Contaminated reference0.002

Degraded reference0.010

Quantum-kernel risk 1.4%

Benchmark discipline: this kernel is scored against PLS-DA, XGBoost, 1D-CNN, spectral transformer and RBF-SVM on an identical split before any advantage is claimed.

Neural decision OS

QUBO pathway routing

H = 0.9·C_transport + 1.1·C_energy + 1.6·C_contamination + 1.3·CO₂ − 1·V_recovered + penalties

Pathway	H	Recovered value	CO₂e	Feasibility
▸ C · Chemical / feedstock recycling	-0.949	₹29,120	+977.6 kg	feasible
D · RDF / waste-to-energy	0.098	₹9,360	+322.4 kg	feasible
B · Mechanical recycling	0.136	₹42,640	+1674.4 kg	blocked · purity 75.7% < 80% required
E · Landfill (baseline)	0.362	₹0	-124.8 kg	feasible
A · Food-grade rPET recovery	1.952	₹54,080	+2017.6 kg	blocked · purity 75.7% < 93% required

Decision

C · Chemical / feedstock recycling

Route this batch to C · Chemical / feedstock recycling. All feasibility gates pass on this batch.

Recovered value

₹29,120

CO₂e avoided (MRV)

1143.8 kg

Confidence

98.6%

SHAP contributions

FTIR carbonyl oxidation index-0.23

HPLC target purity+0.19

Transport distance to hub-0.03

Foreign material fraction-0.03

NIR moisture-0.02

Raman microbial / biofilm band-0.02

Instrument drift penalty-0.01

WHAT happened · WHY · HOW certain · WHAT to do — answered for every recommendation.

Carbon MRV

Tamper-evident event chain

CO₂e,avoided = baseline − project − leakage

Baseline emissions: 1222.0 kg
Project emissions: −76.1 kg
Leakage (transport): −2.1 kg
Net avoided: 1143.8 kg

Hashᵢ = H(Eventᵢ ‖ Hashᵢ₋₁) · demo chain lives in this browser tab only

No events yet — commit a routed batch and each entry links to the previous hash.

GraphRAG + edge SLM

Retrieve first, then speak

FARM_101
  ├─GROWS→ WHEAT
  ├─HAS_SOIL→ SOIL_44
  ├─GENERATES→ STRAW_701
  └─SUPPLIES→ FPO_7

STRAW_701
  ├─SPECTRAL_CLASS→ CELLULOSE_HIGH
  ├─SUITABLE_FOR→ BIOCOMPOSITE
  └─AVOIDS→ OPEN_BURNING

BIOCOMPOSITE_1
  ├─DISPLACES→ PETRO_PLASTIC
  └─REDUCES→ CO2E

Retrieval chain

sensor anomaly → vector search → knowledge graph → RAG → SLM → verified nudge

Grounding corpus: crop protocol, soil threshold, FPO SOP, HPLC method, polymer reference spectrum, water reuse standard, equipment manual, circularity SOP. The edge model may not invent an instruction that is not in the retrieved set.

Example graph query

“Which Nashik FPOs have low-moisture crop residue with >70% suitability for bio-composite production within 50 km of a processing hub?”

→ 3 FPOs · 812 t residue · mean moisture 16.4% · P(suitable) 0.78 · hub 41 km

Neural feedback loop

Sense → predict → act → confirm → retrain

Sense

Four spectral streams + IoT + satellite, synchronised per sample.

Predict & classify

Fusion embedding, quantum feature map, kernel comparison.

Recommend & act

One instruction with a deadline, never a raw sensor dump.

Observe & confirm

Farmer inspection, agronomist result, lab confirmation → ground truth.

Retrain & recalibrate

Field-specific model update plus instrument recalibration on drift.

Drift monitor tracks D_drift(P_train, P_live) across baseline shift, wavelength shift, detector ageing, humidity, temperature and sample heterogeneity — and forces recalibration above threshold instead of silently degrading. Current drift 8% · within tolerance.

Evidence discipline

Simulation ≠ prototype ≠ field validation ≠ deployment

Simulation

Implemented here

Deterministic synthetic spectra + classical quantum-feature-map emulation. No hardware quantum run.

Prototype validation

Bench, in progress

Reference polymers, cullet and reclaimed-water panels against lab ground truth; RMSEP / R² reported per modality.

Field validation

Planned

Edge gateway at FPO + MRF sites; controlled measurement records, calibration protocol, drift logs.

Commercial deployment

Not claimed

Latency, cost, acreage and carbon numbers only quoted once deployment records support them.

North-star KPIs

Circular-economy outcomes, not just model accuracy

CircularityImpact = f(WasteDiverted, CO₂Avoided, WaterSaved, VirginMaterialAvoided, FarmerIncome, RecoveredValue)

Dimension	KPI	Target	Evidence stage
Agriculture	Water saved per acre	≥ 25%	Field pilot
Inputs	kg fertiliser avoided / acre	≥ 18%	Field pilot
Waste	Tonnes diverted from disposal	1,200 t / yr	Prototype
Plastics	Polymer recovery purity	≥ 93% rPET	Bench
Glass	Cullet recovery rate	≥ 88%	Bench
Biomass	Tonnes prevented from burning	900 t / yr	Field pilot
Energy	MJ / kWh recovered	14 MJ/kg straw	Bench
Carbon	tCO₂e avoided (MRV-verified)	3,400 t / yr	Prototype
Income	₹ incremental farmer / FPO income	₹ 9,800 / acre	Field pilot
AI	Precision / recall / calibration (ECE)	0.92 / 0.90 / <0.05	Bench
Spectroscopy	RMSEP / R² / SNR	R² ≥ 0.94	Bench
Quantum	Kernel accuracy vs classical baseline	Parity or better, measured	Simulation
Operations	Edge inference latency	< 40 ms p95	Simulation
Traceability	% digitally verified lots	100% of routed batches	Prototype

Cohort positioning

AI-Quantum Circular Bioeconomy OS for smallholders and rural material recovery

Climate-smart agriculture

›Precision agriculture
›Lot-level traceability
›Food-waste valorisation
›Agri-to-energy routing
›Water recycling gates

Plastic circularity

›AI waste segregation
›NIR polymer identification
›FTIR degradation index
›Alternative biomaterials
›Waste-to-energy optimisation

Glass circularity

›AI cullet sorting
›Contamination classification
›Colour compatibility scoring
›Recovery optimisation
›Reverse-supply-chain traceability

Demonstrator chain

field / recovery site → sensors + spectrometer + camera → ACOCP edge gateway (AI · quantum-ready · rules) → decision engine → farmer app | recycler app | FPO command → climate cloud → digital twin · RAG · MRV → ESG / carbon / finance

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